Identifying Risk of Future Asthma Attacks Using UK Medical Record Data: A Respiratory Effectiveness Group Initiative

A Simulation Study of the Effect of Clinical Characteristics and Treatment Choice on Reliever Medication Use, Symptom Control and Exacerbation Risk in Moderate-Severe Asthma

INTRODUCTION

The relationship between immediate symptom control, reliever medication use and exacerbation risk on treatment response and factors that modify it have not been assessed in an integrated manner. Here we apply simulation scenarios to evaluate the effect of individual baseline characteristics on treatment response in patients with moderate-severe asthma on regular maintenance dosing monotherapy with fluticasone propionate (FP) or combination therapy with fluticasone propionate/salmeterol (FP/SAL) or budesonide/formoterol (BUD/FOR).

METHODS

Reduction in reliever medication use (puffs/24 h), change in symptom control scores (ACQ-5), and annualised exacerbation rate over 12 months were simulated in a cohort of patients with different baseline characteristics (e.g. time since diagnosis, asthma control questionnaire (ACQ-5) symptom score, smoking status, body mass index (BMI) and sex) using drug-disease models derived from large phase III/IV clinical studies.

RESULTS

Simulation scenarios show that being a smoker, having higher baseline ACQ-5 and BMI, and long asthma history is associated with increased reliever medication use (p < 0.01). This increase correlates with a higher exacerbation risk and higher ACQ-5 scores over the course of treatment, irrespective of the underlying maintenance therapy. Switching non-responders to ICS monotherapy to combination therapy after 3 months resulted in immediate reduction in reliever medication use (i.e. 1.3 vs. 1.0 puffs/24 h for FP/SAL and BUD/FOR, respectively). In addition, switching patients with ACQ-5 > 1.5 at baseline to FP/SAL resulted in 34% less exacerbations than those receiving regular dosing BUD/FOR (p < 0.01).

CONCLUSIONS

We have identified baseline characteristics of patients with moderate to severe asthma that are associated with greater reliever medication use, poor symptom control and higher exacerbation risk. Moreover, the effects of different inhaled corticosteroid (ICS)/long-acting beta agonist (LABA) combinations vary significantly when considering long-term treatment performance. These factors should be considered in clinical practice as a basis for personalised management of patients with moderate-severe asthma symptoms.

Sex differences in asthma control, lung function and exacerbations: the ATLANTIS study

BACKGROUND

Asthma is a heterogeneous disease with a prevalence and severity that differs between male and female patients.

QUESTION

What are differences between male and female patients with asthma with regard to asthma control, lung function, inflammation and exacerbations?

METHODS

We performed a post hoc analysis in the ATLANTIS (Assessment of Small Airways Involvement in Asthma) study, an observational cohort study including patients with asthma from nine countries with a follow-up of 1 year during which patients were characterised with measures of large and small airway function, questionnaires, inflammation and imaging. We compared differences in baseline characteristics and longitudinal outcomes between male and female patients with asthma.

RESULTS

773 patients were enrolled; 450 (58%) of these were female. At baseline, female patients with asthma were in higher Global Initiative for Asthma (GINA) steps (p=0.042), had higher Asthma Control Questionnaire 6 (F: 0.83; M: 0.66, p<0.001) and higher airway resistance as reflected by uncorrected impulse oscillometry outcomes (ie, R5-R20: F: 0.06; M: 0.04 kPa/L/s, p=0.002). Male patients with asthma had more severe airway obstruction (forced expiratory volume in 1 s/forced vital capacity % predicted: F: 91.95; M: 88.33%, p<0.01) and more frequently had persistent airflow limitation (F: 27%; M: 39%, p<0.001). Blood neutrophils were significantly higher in female patients (p=0.014). With Cox regression analysis, female sex was an independent predictor for exacerbations.

INTERPRETATION

We demonstrate that female patients are in higher GINA steps, exhibit worse disease control, experience more exacerbations and demonstrate higher airway resistance compared with male patients. The higher exacerbation risk was independent of GINA step and blood eosinophil level. Male patients, in turn, have a higher prevalence of persistent airflow limitation and more severe airflow obstruction. These findings show sex can affect clinical phenotyping and outcomes in asthma.

TRIAL REGISTRATION NUMBER

NCT02123667.

State of the art: Alternative overlap syndrome-asthma and obstructive sleep apnea

In the general population, Bronchial Asthma (BA) and Obstructive Sleep Apnea (OSA) are among the most prevalent chronic respiratory disorders. Significant epidemiologic connections and complex pathogenetic pathways link these disorders via complex interactions at genetic, epigenetic, and environmental levels. The coexistence of BA and OSA in an individual likely represents a distinct syndrome, that is, a collection of clinical manifestations attributable to several mechanisms and pathobiological signatures. To avoid terminological confusion, this association has been named alternative overlap syndrome (vs overlap syndrome represented by the chronic obstructive pulmonary disease-OSA association). This comprehensive review summarizes the complex, often bidirectional links between the constituents of the alternative overlap syndrome. Cross-sectional, population, or clinic-based studies are unlikely to elucidate causality or directionality in these relationships. Even longitudinal epidemiological evaluations in BA cohorts developing over time OSA, or OSA cohorts developing BA during follow-up cannot exclude time factors or causal influence of other known or unknown mediators. As such, a lot of pathophysiological interactions described here have suggestive evidence, biological plausibility, potential or actual directionality. By showcasing existing evidence and current knowledge gaps, the hope is that deliberate, focused, and collaborative efforts in the near-future will be geared toward opportunities to shine light on the unknowns and accelerate discovery in this field of health, clinical care, education, research, and scholarly endeavors.

Contribution of Obstructive Sleep Apnea to Asthmatic Airway Inflammation and Impact of Its Treatment on the Course of Asthma

Asthma and obstructive sleep apnea (OSA) are very common respiratory disorders in the general population. Beyond their high prevalence, shared risk factors, and genetic linkages, bidirectional relationships between asthma and OSA exist, each disorder affecting the other's presence and severity. The author reviews here some of the salient links between constituents of the alternative overlap syndrome, that is, OSA comorbid with asthma, with an emphasis on the effects of OSA or its treatment on inflammation in asthma. In the directional relationship from OSA toward asthma, beyond direct influences, multiple factors and comorbidities seem to contribute.

The Association Between Short-Acting β2-Agonist Over-Prescription, and Patient-Reported Acquisition and Use on Asthma Control and Exacerbations: Data from Australia

INTRODUCTION

In Australia, short-acting β2-agonists (SABA) are available both over the counter (OTC) and on prescription. This ease of access may impact SABA use in the Australian population. Our aim was to assess patterns and outcome associations of prescribed, acquired OTC and reported use of SABA by Australians with asthma.

METHODS

This was a cross-sectional study, using data derived from primary care electronic medical records (EMRs) and patient completed questionnaires within Optimum Patient Care Research Database Australia (OPCRDA). A total of 720 individuals aged ≥ 12 years with an asthma diagnosis in their EMRs and receiving asthma therapy were included. The annual number of SABA inhalers authorised on prescription, acquired OTC and reported, and the association with self-reported exacerbations and asthma control were investigated.

RESULTS

92.9% (n = 380/409) of individuals issued with SABA prescription were authorised ≥ 3 inhalers annually, although this differed from self-reported usage. Of individuals reporting SABA use (n = 546) in the last 12 months, 37.0% reported using ≥ 3 inhalers. These patients who reported SABA overuse experienced 2.52 (95% confidence interval [CI] 1.73-3.70) times more severe exacerbations and were 4.51 times (95% CI 3.13-6.55) more likely to have poor asthma control than those who reported using 1-2 SABA inhalers. Patients who did not receive SABA on prescription (43.2%; n = 311/720) also experienced 2.71 (95% CI 1.07-7.26) times more severe exacerbations than those prescribed 1-2 inhalers. Of these patients, 38.9% reported using OTC SABA and other prescription medications, 26.4% reported using SABA OTC as their only asthma medication, 13.2% were prescribed other therapies but not SABA OTC and 14.5% were not using any medication.

CONCLUSION

Both self-reported SABA overuse and zero SABA prescriptions were associated with poor asthma outcomes. The disconnect between prescribing authorisation, OTC availability and actual use, make it difficult for clinicians to quantify SABA use.

Effect of Individual Patient Characteristics and Treatment Choices on Reliever Medication Use in Moderate-Severe Asthma: A Poisson Analysis of Randomised Clinical Trials

INTRODUCTION

Even though increased use of reliever medication, including short-acting beta agonists (SABA), provides an indirect measure of symptom worsening, there have been limited efforts to assess how different patterns of reliever use correlate with symptom control and future risk of exacerbations. Here, we evaluate the effect of individual baseline characteristics on reliever use in patients with moderate-severe asthma on regular maintenance therapy with fluticasone propionate (FP) or combination therapy with fluticasone propionate/salmeterol (FP/SAL) or budesonide/formoterol (BUD/FOR).

METHODS

A drug-disease model describing the number of 24-h puffs and overnight occasions was developed with data from five clinical studies (N = 6212). The model was implemented using a nonlinear mixed effects approach and a Poisson function, considering clinical and demographic baseline characteristics. Goodness of fit and model predictive performance were assessed. Heatmaps were created to summarise the effect of concurrent baseline factors on reliever utilisation.

RESULTS

The final model accurately described individual patterns of reliever use, which is significantly increased with time since diagnosis, smoking, higher Asthma Control Questionnaire (ACQ-5) score and higher body mass index (BMI) at baseline. Whilst the number of puffs decreases slowly after an initial drop relative to the start of treatment, exacerbating patients utilise significantly more reliever than those who do not exacerbate. The mean effect of FP/SAL (median dose: 250/50 μg BID) on reliever use was slightly higher than that of BUD/FOR (median dose: 160/4.5 μg BID), i.e. a 75.3% vs 69.3% reduction in reliever use, respectively.

CONCLUSIONS

The availability of individual-level patient data in conjunction with a parametric approach enabled the characterisation of interindividual differences in the patterns of reliever use in patients with moderate-severe asthma. Taken together, individual demographic and clinical characteristics, as well as exacerbation history, can be considered an indicator of the degree of asthma control. High SABA reliever use suggests suboptimal clinical management of patients on maintenance therapy.

Development of theoretically informed audit and feedback: An exemplar from a complex implementation strategy to improve asthma self-management in UK primary care

RATIONALE

Audit and feedback is an evidence-based implementation strategy, but studies reporting the use of theory to guide design elements are limited.

AIMS AND OBJECTIVES

Within the context of a programme of research aiming to improve the implementation of supported asthma self-management in UK primary care (IMPlementing IMProved Asthma self-management as RouTine [IMP2 ART]), we aimed to design and develop theoretically-informed audit and feedback that highlighted supported asthma self-management provision and areas for improvement in primary care general practices.

METHOD

Aligned with the Medical Research Council (MRC) complex intervention framework, the audit and feedback was developed in three phases: (1) Development: literature and theory exploration, and prototype audit and feedback design; (2) Feasibility: eliciting feedback on the audit and feedback from general practice staff (n = 9); (3) Prepiloting: delivering the audit and feedback within the IMP2 ART implementation strategy (incorporating patient and professional resources and an asthma review template) and eliciting clinician feedback (n = 9).

RESULTS

Audit and feedback design was guided by and mapped to existing literature suggestions and theory (e.g., Theoretical Domains Framework, Behaviour Change Technique Taxonomy). Feedback on the prototype audit and feedback confirmed feasibility but identified some refinements (a need to highlight supporting self-management and importance of asthma action plans). Prepiloting informed integration with other IMP2 ART programme strategies (e.g., patient resources and professional education).

CONCLUSION

We conclude that a multistage development process including theory exploration and mapping, contributed to the design and delivery of the audit and feedback. Aligned with the MRC framework, the IMP2 ART strategy (incorporating the audit and feedback) is now being tested in a UK-wide cluster randomised controlled trial.

Machine Learning Approaches to Predict Asthma Exacerbations: A Narrative Review

The implementation of artificial intelligence (AI) and machine learning (ML) techniques in healthcare has garnered significant attention in recent years, especially as a result of their potential to revolutionize personalized medicine. Despite advances in the treatment and management of asthma, a significant proportion of patients continue to suffer acute exacerbations, irrespective of disease severity and therapeutic regimen. The situation is further complicated by the constellation of factors that influence disease activity in a patient with asthma, such as medical history, biomarker phenotype, pulmonary function, level of healthcare access, treatment compliance, comorbidities, personal habits, and environmental conditions. A growing body of work has demonstrated the potential for AI and ML to accurately predict asthma exacerbations while also capturing the entirety of the patient experience. However, application in the clinical setting remains mostly unexplored, and important questions on the strengths and limitations of this technology remain. This review presents an overview of the rapidly evolving landscape of AI and ML integration into asthma management by providing a snapshot of the existing scientific evidence and proposing potential avenues for future applications.

Use of feature importance statistics to accurately predict asthma attacks using machine learning: A cross-sectional cohort study of the US population

BACKGROUND

Asthma attacks are a major cause of morbidity and mortality in vulnerable populations, and identification of associations with asthma attacks is necessary to improve public awareness and the timely delivery of medical interventions.

OBJECTIVE

The study aimed to identify feature importance of factors associated with asthma in a representative population of US adults.

METHODS

A cross-sectional analysis was conducted using a modern, nationally representative cohort, the National Health and Nutrition Examination Surveys (NHANES 2017-2020). All adult patients greater than 18 years of age (total of 7,922 individuals) with information on asthma attacks were included in the study. Univariable regression was used to identify significant nutritional covariates to be included in a machine learning model and feature importance was reported. The acquisition and analysis of the data were authorized by the National Center for Health Statistics Ethics Review Board.

RESULTS

7,922 patients met the inclusion criteria in this study. The machine learning model had 55 out of a total of 680 features that were found to be significant on univariate analysis (P<0.0001 used). In the XGBoost model the model had an Area Under the Receiver Operator Characteristic Curve (AUROC) = 0.737, Sensitivity = 0.960, NPV = 0.967. The top five highest ranked features by gain, a measure of the percentage contribution of the covariate to the overall model prediction, were Octanoic Acid intake as a Saturated Fatty Acid (SFA) (gm) (Gain = 8.8%), Eosinophil percent (Gain = 7.9%), BMXHIP-Hip Circumference (cm) (Gain = 7.2%), BMXHT-standing height (cm) (Gain = 6.2%) and HS C-Reactive Protein (mg/L) (Gain 6.1%).

CONCLUSION

Machine Learning models can additionally offer feature importance and additional statistics to help identify associations with asthma attacks.

Development of an Asthma Exacerbation Risk Prediction Model for Conversational Use by Adults in England

Background

Improving accurate risk assessment of asthma exacerbations, and reduction via relevant behaviour change among people with asthma could save lives and reduce health care costs. We developed a simple personalised risk prediction model for asthma exacerbations using factors collected in routine healthcare data for use in a risk modelling feature for automated conversational systems.

Methods

We used pseudonymised primary care electronic healthcare records from the Clinical Practice Research Datalink (CPRD) Aurum database in England. We combined variables for prediction of asthma exacerbations using logistic regression including age, gender, ethnicity, Index of Multiple Deprivation, geographical region and clinical variables related to asthma events.

Results

We included 1,203,741 patients divided into three cohorts to implement temporal validation: 898,763 (74.7%) in the training sample, 226,754 (18.8%) in the testing sample and 78,224 (6.5%) in the validation sample. The Area under the ROC curve (AUC) for the full model was 0.72 and for the restricted model was 0.71. Using a cut-off point of 0.1, approximately 27 asthma reviews by clinicians per 100 patients would be prevented compared with a strategy that all patients are regarded as high risk. Compared with patients without an exacerbation, patients who exacerbated were older, more likely to be female, prescribed more SABA and ICS in the preceding 12 months, have history of GORD, COPD, anxiety, depression, live in very deprived areas and have more severe disease.

Conclusion

Using information available from routinely collected electronic healthcare record data, we developed a model that has moderate ability to separate patients who had an asthma exacerbation within 3 months from their index date from patients who did not. When comparing this model with a simplified model with variables that can easily be self-reported through a WhatsApp chatbot, we have shown that the predictive performance of the model is not substantially different.

The Anti-Inflammatory Reliever (AIR) Algorithm Study: a protocol for a single-group study of an AIR stepwise approach to the treatment of adult asthma

Background

The stepwise approach to long-term asthma management, which traditionally incorporates short-acting β2-agonist reliever therapy, has been a core feature of asthma guidelines for over 30 years. There have been no studies, however, directly investigating the use of an entire guideline-recommended track. Recently, inhaled corticosteroid-formoterol has been recommended as the preferred reliever therapy in adult asthma, in accordance with a stepwise "Anti-Inflammatory Reliever" (AIR) treatment track.

Objective

The aim of this study was to evaluate the AIR stepwise approach recommended by the New Zealand adolescent and adult asthma guidelines, in combination with a novel algorithm for transitioning between treatment steps.

Methods

This 52-week, open-label, single-group study will recruit 100 adults aged 18 to 75 years with mild, moderate and moderate-severe asthma (ACTRN12620001010987). Participants will be allocated to budesonide-formoterol 200/6 µg, one actuation as needed (Step 1), one actuation twice daily and as needed (Step 2), or two actuations twice daily and one as needed (Step 3). Treatment steps will be adjusted throughout the study, in response to reliever use and asthma attacks, according to a stepwise AIR algorithm. Following a 26-week period of investigator-led transitions, participants will adjust their own treatment step. The primary outcome is participant satisfaction as measured by the Global Satisfaction score of the Treatment Satisfaction Questionnaire for Medication. Secondary outcomes will assess efficacy and safety, and describe patterns of medication use and participant flow through the treatment steps.

Conclusion

This is the first trial to assess the AIR treatment track and algorithm. The results will provide knowledge to guide the clinical use of this approach.

Handling Class Imbalance in Machine Learning-based Prediction Models: A Case Study in Asthma Management

A data-driven prediction tool has the potential to provide early warning of an asthma attack and improve asthma management and outcomes. Most previous machine learning (ML)-based studies for asthma attack prediction have reported a severe class imbalance, with major implications for model performance. We aimed to undertake a systematic comparison of several class imbalance handling techniques in the context of risk prediction models for asthma prognosis. We used data from 9,835 asthma patients extracted from the Medical Information Mart for Intensive Care (MIMIC) IV database and deployed five class imbalance handling methods based on synthetic minority oversampling technique (SMOTE) and cost function customisation. We then compared their performances in improving two-class classifier models developed using logistic regression (LR) and extreme gradient boosting (XGBoost) for three different prediction tasks with varying severity of class imbalance (proportion of majority class ranging from 90.86% to 98.98%). The cost function customisation technique substantially outperformed the SMOTE-based methods in all tasks. XGBoost combined with cost function customisation achieved the highest prediction performance for the outcome with the most extreme class imbalance ratio (AUC = 0.72). Our findings suggest that the cost function customisation-based approach to tackle class imbalance provides substantially better performance compared to oversampling in the context of asthma management.Clinical Relevance- This study underscores the challenge of class imbalance in the context of prediction tools to improve asthma management and outcomes and provides a methodological solution that addresses the challenge. Accurate asthma prediction tools can provide early warning and potentially prevent deterioration thereby improving the quality of life of patients with asthma.

Questions in Mild Asthma: An Official American Thoracic Society Research Statement

Background: Patients with mild asthma are believed to represent the majority of patients with asthma. Disease-associated risks such as exacerbations, lung function decline, and death have been understudied in this patient population. There have been no prior efforts from major societies to describe research needs in mild asthma. Methods: A multidisciplinary, diverse group of 24 international experts reviewed the literature, identified knowledge gaps, and provided research recommendations relating to mild asthma definition, pathophysiology, and management across all age groups. Research needs were also investigated from a patient perspective, generated in conjunction with patients with asthma, caregivers, and stakeholders. Of note, this project is not a systematic review of the evidence and is not a clinical practice guideline. Results: There are multiple unmet needs in research on mild asthma driven by large knowledge gaps in all areas. Specifically, there is an immediate need for a robust mild asthma definition and an improved understanding of its pathophysiology and management strategies across all age groups. Future research must factor in patient perspectives. Conclusions: Despite significant advances in severe asthma, there remain innumerable research areas requiring urgent attention in mild asthma. An important first step is to determine a better definition that will accurately reflect the heterogeneity and risks noted in this group. This research statement highlights the topics of research that are of the highest priority. Furthermore, it firmly advocates the need for engagement with patient groups and for more support for research in this field.

Real-Life Effectiveness of Benralizumab, Mepolizumab and Omalizumab in Severe Allergic Asthma Associated with Nasal Polyps

Biological therapies are available for the treatment of the severe allergic asthma (SAA) with blood eosinophil count ≥ 0.3 × 10⁹/L. Several of them also showed benefits on nasal polyps (NP), one of the most frequent comorbidities of the severe asthma, but comparative studies on their effectiveness in the association SAA-NP are currently lacking. The aim of this study is to compare the effectiveness of benralizumab, mepolizumab and omalizumab in patients with SAA-NP in real-life settings. A retrospective, observational, multicenter real-life study was realized including patients with SAA-NP treated by benralizumab, mepolizumab or omalizumab for 6 months. We analysed the nasal and respiratory symptoms, the number of asthma attacks and salbutamol use/week, acute sinusitis and severe exacerbation rates, the asthma control score, the lung function parameters, the NP endoscopic score, the sinus imaging and the blood eosinophil count 6 months before and after treatment. Seventy-two patients with SAA-NP were included: 16 treated by benralizumab, 21 by mepolizumab and 35 by omalizumab. After 6 months of treatment, almost all studied parameters were improved (except sinus imaging) with a greater effect of omalizumab on the nasal pruritus (p = 0.001) and more benefits of benralizumab on exacerbations rate, asthma attacks per week and lung function (all p < 0.05). Benralizumab and mepolizumab were more effective to improve the NP endoscopic score and the blood eosinophil count (both p < 0.001). All three biological therapies showed effectiveness by improving asthma and nasal outcomes in patients with SAA-NP. Several differences have been found that should be confirmed by larger comparative studies.

Asthma

Asthma is one of the most common chronic non-communicable diseases worldwide and is characterised by variable airflow obstruction, causing dyspnoea and wheezing. Highly effective therapies are available; asthma morbidity and mortality have vastly improved in the past 15 years, and most patients can attain good asthma control. However, undertreatment is still common, and improving patient and health-care provider understanding of when and how to adjust treatment is crucial. Asthma management consists of a cycle of assessment of asthma control and risk factors and adjustment of medications accordingly. With the introduction of biological therapies, management of severe asthma has entered the precision medicine era-a shift that is driving clinical ambitions towards disease remission. Patients with severe asthma often have co-existing conditions contributing to their symptoms, mandating a multidimensional management approach. In this Seminar, we provide a clinically focused overview of asthma; epidemiology, pathophysiology, diagnosis, and management in children and adults.

Defining clinical subtypes of adult asthma using electronic health records: Analysis of a large UK primary care database with external validation

INTRODUCTION

Asthma is one of the commonest chronic conditions in the world. Subtypes of asthma have been defined, typically from clinical datasets on small, well-characterised subpopulations of asthma patients. We sought to define asthma subtypes from large longitudinal primary care electronic health records (EHRs) using cluster analysis.

METHODS

In this retrospective cohort study, we extracted asthma subpopulations from the Optimum Patient Care Research Database (OPCRD) to robustly train and test algorithms, and externally validated findings in the Secure Anonymised Information Linkage (SAIL) Databank. In both databases, we identified adults with an asthma diagnosis code recorded in the three years prior to an index date. Train and test datasets were selected from OPCRD using an index date of Jan 1, 2016. Two internal validation datasets were selected from OPCRD using index dates of Jan 1, 2017 and 2018. Three external validation datasets were selected from SAIL using index dates of Jan 1, 2016, 2017 and 2018. Each dataset comprised 50,000 randomly selected non-overlapping patients. Subtypes were defined by applying multiple correspondence analysis and k-means cluster analysis to the train dataset, and were validated in the internal and external validation datasets.

RESULTS

We defined six asthma subtypes with clear clinical interpretability: low inhaled corticosteroid (ICS) use and low healthcare utilisation (30% of patients); low-to-medium ICS use (36%); low-to-medium ICS use and comorbidities (12%); varied ICS use and comorbid chronic obstructive pulmonary disease (4%); high (10%) and very high ICS use (7%). The subtypes were replicated with high accuracy in internal (91-92%) and external (84-86%) datasets.

CONCLUSION

Asthma subtypes derived and validated in large independent EHR databases were primarily defined by level of ICS use, level of healthcare use, and presence of comorbidities. This has important clinical implications towards defining asthma subtypes, facilitating patient stratification, and developing more personalised monitoring and treatment strategies.

Different clinical characteristics of current smokers and former smokers with asthma: a cross-sectional study of adult asthma patients in China

Smoking is a trigger for asthma, which has led to an increase in asthma incidence in China. In smokers, asthma management starts with smoking cessation. Data on predictors of smoking cessation in Chinese patients with asthma are scarce. The objective of this study was to find the differences in clinical characteristics between current smokers and former smokers with asthma in order to identify factors associated with smoking cessation. Eligible adults with diagnosed asthma and smoking from the hospital outpatient clinics (n = 2312) were enrolled and underwent a clinical evaluation, asthma control test (ACT), and pulmonary function test. Information on demographic and sociological data, lung function, laboratory tests, ACT and asthma control questionnaire (ACQ) scores was recorded. Patients were divided into a current smokers group and a former smokers group based on whether they had quit smoking. Logistic regression analysis was used to analyze the factors associated with smoking cessation. Of all patients with asthma, 34.6% were smokers and 65.4% were former smokers, and the mean age was 54.5 ± 11.5 years. Compared with current smokers, the former smokers were older, had longer duration of asthma, had higher ICS dose, had more partially controlled and uncontrolled asthma, had more pack-years, had smoked for longer, and had worse asthma control. The logistic regression model showed that smoking cessation was positively correlated with age, female sex, pack-years, years of smoking, partially controlled asthma, uncontrolled asthma, and body mass index (BMI), but was negatively correlated with ACT, FEV₁, FEV₁%predicted, and widowed status. More than 30% of asthma patients in the study were still smoking. Among those who quit smoking, many quit late, often not realizing they need to quit until they have significant breathing difficulties. The related factors of smoking cessation identified in this study indicate that there are still differences between continuing smokers and former smokers, and these factors should be focused on in asthma smoking cessation interventions to improve the prognosis of patients with asthma.

Childhood blood eosinophils and symptoms of allergic disorders: a cross-sectional study in Southern China

PURPOSE

The relationship between childhood blood eosinophils and subtypes of allergic diseases remains understudied. This study aimed to examine the associations between childhood blood eosinophils and subtypes of asthma, rhinitis and dermatitis, as well as the modifying effect of age.

METHODS

We obtained concurrent blood cell counts and serum Immunoglobulin E (IgE) test results in 5026 children (0-13, years) from First Affiliated Hospital of Guangzhou Medical University from 2014 to 2019. Generalized additive models with multivariable adjustments were utilized to model the exposure-response relationship between eosinophils and allergic symptoms. The robustness of the association was assessed in two age categories (<6, 6-13 years).

RESULTS

The association of eosinophils with allergic asthma/rhinitis was positively nonlinear, with a plateau at levels of Q₄ (≥0.51, 10⁹/L). Conversely, exposure-response curves between eosinophils and the risk of non-allergic asthma and rhinitis were negatively linear, and especially, became statistically significant when levels of eosinophils were larger than Q₃ (≥0.30, 10⁹/L). Compared with their counterparts, school-aged children (6-13, years) with a higher level of blood eosinophils (≥0.35, 10⁹/L) were more likely to suffer from allergic asthma [relative excess risk due to interaction (RERI), 2.51; 95% CI, 1.24-3.78], allergic rhinitis (RERI, 2.79; 95% CI, 1.14-4.45) but not allergic dermatitis (RERI not significant).

CONCLUSION

Higher eosinophil counts were associated with the increased risk of allergic subtype symptoms and the decreased risk of non-allergic subtypes in children. Moreover, the associations between eosinophils and allergic asthma/rhinitis were accentuated in the school-aged child. These findings may contribute to providing novel insights for clinical administration relevance of allergic-related symptoms.Key messages:There was a positively nonlinear association between childhood eosinophils and allergic asthma/rhinitis.Age modified the associations between eosinophils and allergy-related outcomes. The associations of eosinophil with allergic asthma/rhinitis accentuated in the school-aged child (6-13, years).

Derivation of asthma severity from electronic prescription records using British thoracic society treatment steps

Background:

Asthma severity is typically assessed through a retrospective assessment of the treatment required to control symptoms and to prevent exacerbations. The joint British Thoracic Society and Scottish Intercollegiate Guidelines Network (BTS/SIGN) guidelines encourage a stepwise approach to pharmacotherapy, and as such, current treatment step can be considered as a severity categorisation proxy. Briefly, the steps for adults can be summarised as: no controller therapy (Step 0), low-strength Inhaled Corticosteroids (ICS; Step 1), ICS plus Long-Acting Beta-2 Agonist (LABA; Step 2), medium-dose ICS + LABA (Step 3), and finally either an increase in strength or additional therapies (Step 4). This study aimed to investigate how BTS/SIGN Steps can be estimated from across a large cohort using electronic prescription records, and to describe the incidence of each BTS/SIGN Step in a general population.

Methods:

There were 41,433,707 prescriptions, for 671,304 individuals, in the Asthma Learning Health System Scottish cohort, between 1/2009 and 3/2017. Days on which an individual had a prescription for at least one asthma controller (preventer) medication were labelled prescription events. A rule-based algorithm was developed for extracting the strength and volume of medication instructed to be taken daily from free-text data fields. Asthma treatment regimens were categorised by the combination of medications prescribed in the 120 days preceding any prescription event and categorised into BTS/SIGN treatment steps.

Results:

Almost 4.5 million ALHS prescriptions were for asthma controllers. 26% of prescription events had no inhaled corticosteroid prescriptions in the preceding 120 days (Step 0), 16% were assigned to BTS/SIGN Step 1, 7% to Step 2, 21% to Step 3, and 30% to Step 4. The median days spent on a treatment step before a step-down in treatment was 297 days, whereas a step-up only took a median of 134 days.

Conclusion

We developed a reproducible methodology enabling researchers to estimate BTS/SIGN asthma treatment steps in population health studies, providing valuable insights into population and patient-specific trajectories, towards improving the management of asthma.

Predicting asthma attacks using connected mobile devices and machine learning: the AAMOS-00 observational study protocol

INTRODUCTION

Supported self-management empowering people with asthma to detect early deterioration and take timely action reduces the risk of asthma attacks. Smartphones and smart monitoring devices coupled with machine learning could enhance self-management by predicting asthma attacks and providing tailored feedback.We aim to develop and assess the feasibility of an asthma attack predictor system based on data collected from a range of smart devices.

METHODS AND ANALYSIS

A two-phase, 7-month observational study to collect data about asthma status using three smart monitoring devices, and daily symptom questionnaires. We will recruit up to 100 people via social media and from a severe asthma clinic, who are at risk of attacks and who use a pressurised metered dose relief inhaler (that fits the smart inhaler device).Following a preliminary month of daily symptom questionnaires, 30 participants able to comply with regular monitoring will complete 6 months of using smart devices (smart peak flow meter, smart inhaler and smartwatch) and daily questionnaires to monitor asthma status. The feasibility of this monitoring will be measured by the percentage of task completion. The occurrence of asthma attacks (definition: American Thoracic Society/European Respiratory Society Task Force 2009) will be detected by self-reported use (or increased use) of oral corticosteroids. Monitoring data will be analysed to identify predictors of asthma attacks. At the end of the monitoring, we will assess users' perspectives on acceptability and utility of the system with an exit questionnaire.

ETHICS AND DISSEMINATION

Ethics approval was provided by the East of England - Cambridge Central Research Ethics Committee. IRAS project ID: 285 505 with governance approval from ACCORD (Academic and Clinical Central Office for Research and Development), project number: AC20145. The study sponsor is ACCORD, the University of Edinburgh.Results will be reported through peer-reviewed publications, abstracts and conference posters. Public dissemination will be centred around blogs and social media from the Asthma UK network and shared with study participants.

Characterisation of the Australian Adult Population Living with Asthma: Severe - Exacerbation Frequency, Long-Term OCS Use and Adverse Effects

Introduction

Asthma poses a significant burden for the Australian population. Understanding severe exacerbation rates, and steroid-related burden for adults diagnosed with asthma stands to offer insights into how this could be reduced.

Methods

Electronic medical records (EMR) and questionnaires from the Optimum Patient Care Research Database Australia (OPCRDA) were utilised retrospectively. OPCRDA is a real-world database with >800,000 medical records from Australian primary care practices. Outcomes were severe asthma exacerbations in Australian adults, over a 12-month period, stratified by Global Initiative for Asthma (GINA) treatment intensity steps, and steroid associated comorbidities.

Results

Of the 7868 adults treated for asthma, 19% experienced at least one severe exacerbation in the last 12-months. Severe exacerbation frequency increased with treatment intensity (≥1 severe exacerbation GINA 1 13%; GINA 4 23%; GINA 5a 33% and GINA 5b 28%). Questionnaire participants reported higher rates of severe exacerbations than suggested from their EMR (32% vs 23%) especially in steps 1, 4 and 5. Patients repeatedly exposed to steroids had an increased risk of osteoporosis (OR 1.95, 95% CI 1.43–2.66) and sleep apnoea (OR 1.78, 95% CI 1.30–2.46).

Conclusion

The Australian population living with GINA 1, 4, 5a and 5b asthma have high severe exacerbation rates and steroid-related burden, especially when compared to other first world countries, with these patients needing alternative strategies or possibly specialist assessment to better manage their condition.

Type 2 inflammation in asthma and other airway diseases

Chronic inflammatory airway diseases, including asthma, chronic rhinosinusitis, eosinophilic COPD and allergic rhinitis are a global health concern. Despite the coexistence of these diseases and their common pathophysiology, they are often managed independently, resulting in poor asthma control, continued symptoms and poor quality of life. Understanding disease pathophysiology is important for best treatment practice, reduced disease burden and improved patient outcomes.

The pathophysiology of type 2 inflammation is driven by both the innate immune system triggered by pollutants, viral or fungal infections involving type 2 innate lymphoid cells (ILC2) and the adaptive immune system, triggered by contact with an allergen involving type 2 T-helper (Th2) cells. Both ILC2 and Th2 cells produce the type-2 cytokines (interleukin (IL)-4, IL-5 and IL-13), each with several roles in the inflammation cascade. IL-4 and IL-13 cause B-cell class switching and IgE production, release of pro-inflammatory mediators, barrier disruption and tissue remodelling. In addition, IL-13 causes goblet-cell hyperplasia and mucus production. All three interleukins are involved in trafficking eosinophils to tissues, producing clinical symptoms characteristic of chronic inflammatory airway diseases.

Asthma is a heterogenous disease; therefore, identification of biomarkers and early targeted treatment is critical for patients inadequately managed by inhaled corticosteroids and long-acting β-agonists alone. The Global Initiative for Asthma guidelines recommend add-on biological (anti IgE, IL-5/5R, IL-4R) treatments for those not responding to standard of care. Targeted therapies, including omalizumab, mepolizumab, reslizumab, benralizumab, dupilumab and tezepelumab, were developed on current understanding of the pathophysiology of type 2 inflammation. These therapies offer hope for improved management of type 2 inflammatory airway diseases.

Asthma is a heterogenous disease. Targeted therapies have been developed based on the pathophysiology of type 2 inflammation. These therapies offer hope for improved management of type 2 inflammatory airway diseases.https://bit.ly/3HCkrvQ

Primary Care Management of Asthma Exacerbations or Attacks: Impact of the COVID-19 Pandemic

The COVID-19 pandemic has brought a renewed focus on appropriate management of chronic respiratory conditions with a heightened awareness of respiratory symptoms and the requirement for differential diagnosis between an asthma attack and COVID-19 infection. Despite early concerns in the pandemic, most studies suggest that well-managed asthma is not a risk factor for more severe COVID-related outcomes, and that asthma may even have a protective effect. Advice on the treatment of asthma and asthma attacks has remained unchanged. This article describes some challenges faced in primary care asthma management in adults and in teenagers, particularly their relevance during a pandemic, and provides practical advice on asthma attack recognition, classification, treatment and continuity of care. Acute attacks, characterised by increased symptoms and reduced lung function, are often referred to as exacerbations of asthma by doctors and nurses but are usually described by patients as asthma attacks. They carry a significant and underestimated morbidity and mortality burden. Many patients experiencing an asthma attack are assessed in primary care for treatment and continuing management. This may require remote assessment by telephone and home monitoring devices, where available, during a pandemic. Differentiation between an asthma attack and a COVID-19 infection requires a structured clinical assessment, taking account of previous medical and family history. Early separation into mild, moderate, severe or life-threatening attacks is helpful for continuing good management. Most attacks can be managed in primary care but when severe or unresponsive to initial treatment, the patient should be appropriately managed until transfer to an acute care facility can be arranged. Good quality care is important to prevent further attacks and must include a follow-up appointment in primary care, proactive regular dosing with daily controller therapy and an understanding of a patient's beliefs and perceptions about asthma to maximise future self-management.

Current unmet needs and potential solutions to uncontrolled asthma

Despite the availability of effective inhaled therapies, many patients with asthma have poor asthma control. Uncontrolled asthma presents a significant burden on the patient and society, and, for many, remains largely preventable. There are numerous reasons why a patient may remain uncontrolled despite access to therapies, including incorrect inhaler technique, poor adherence to treatment, oversight of triggers and suboptimal medical care. Shared decision-making, good patient–clinician communication, supported self-management, multidisciplinary patient education, new technology and risk stratification may all provide solutions to this major unmet need in asthma. Novel treatments such as biologics could benefit patients’ lives, while the investigations into biomarkers, non-Type 2 asthma, treatable traits and disease modification give an exciting glimpse into the future of asthma care.

Despite effective therapies, many patients with asthma have poor asthma control, which is preventable. The benefits of shared decision-making, supported self-management, risk stratification and novel treatments in transforming patient care are reviewed.https://bit.ly/3A386Nm

Qualitative study on perceptions of use of Fractional Exhaled Nitric Oxide (FeNO) in asthma reviews

Current methods to assess asthma and guide inhaled corticosteroid (ICS) dose titration mainly centre on patient-reported symptoms and lung function assessments. However, these methods correlate only weakly with airway inflammation making them unreliable predictors of future exacerbations and ICS requirement. Fractional Exhaled Nitric Oxide (FeNO) is a simple non-invasive objective measure of airways inflammation used predominantly in specialist clinics. Previous qualitative studies have mainly focused on the acceptability of FeNO in secondary care and there is limited insight to support clinicians and patients using FeNO in primary care asthma reviews. This study aimed to explore adult patient with asthma and primary care health care professional (HCP) views on introducing FeNO as part of routine asthma reviews. Twenty-three health care professionals and 22 patients were interviewed over the phone or online. Both groups reported that current asthma reviews are often seen as tick-box exercises and that introducing the FeNO test would make reviews more tailored to the individual patient, rather than relying on subjective patient reports of asthma control. Adults with asthma also highlighted support more open communication and their understanding of asthma, as they desired to feel more engaged in decisions and conversations about their asthma. HCPs reported valuing patient education and empowerment over a paternalistic approach, when time and resources allow. They also recognised FeNO to provide an objective measure of inflammation that could support them in the education and empowerment of patients. FeNO was seen by both groups as a potentially valuable addition to current asthma reviews mainly led by nurses, both for increasing their understanding of current risk of exacerbation and also to provide more tailored and personalised asthma management to patients. Our findings highlighted the need for open and clear communication about how to interpret FeNO results.

An assessment of asthma exacerbations in pediatric patients using a long-acting B2-agonist plus inhaled corticosteroid versus an inhaled corticosteroid alone

Background

An asthma exacerbation is an anticipated sudden worsening of the disease severity, which usually does not respond to conservative therapy. The management of asthma depends on the severity of the disease symptoms, which includes an inhaled corticosteroid (ICS) and a bronchodilator. This study aimed to assess the efficacy of combining a long-acting B2-agonist (LABA) with ICS, compared to ICS alone, to reduce the incidence of asthma exacerbations in pediatric patients, diagnosed with severe persistent asthma.

Methods

A retrospective analysis of the medical records was conducted for 586 children, admitted to the Emergency Department (ED) at King Abdullah Specialized Children Hospital in Riyadh, Saudi Arabia, for the management of severe persistent asthma symptoms, from January 2016 to September 2019.

Results

The majority (n = 480, 81.9%) of the patients received fluticasone (Flovent)® as the standard of care ICS treatment for controlling asthma, and a small proportion (n = 106, 18.1%) were treated with a combination of LABA and ICS. A significant increase in the frequency of recurrent asthma exacerbation episodes occurred in the group receiving ICS alone (98.5%), compared to 67.0% in the combination group (p < 0.0001). Moderate to severe exacerbations were significantly higher in the ICS group compared to the combination group (95.6% versus 84.5%, respectively, p = 0.0005).

Conclusions

The current results confirm the substantial efficacy of the LABA/ICS combination therapy in reducing the incidence and severity of asthma exacerbations in pediatric patients, compared to ICS alone.

Digital healthcare in COPD management: a narrative review on the advantages, pitfalls, and need for further research

Chronic obstructive pulmonary disease (COPD) remains a leading cause of morbidity and mortality despite current treatment strategies which focus on smoking cessation, pulmonary rehabilitation, and symptomatic relief. A focus of COPD care is to encourage self-management, particularly during COVID-19, where much face-to-face care has been reduced or ceased. Digital health solutions may offer affordable and scalable solutions to support COPD patient education and self-management, such solutions could improve clinical outcomes and expand service reach for limited additional cost. However, optimal ways to deliver digital medicine are still in development, and there are a number of important considerations for clinicians, commissioners, and patients to ensure successful implementation of digitally augmented care. In this narrative review, we discuss advantages, pitfalls, and future prospects of digital healthcare, which offer a variety of tools including self-management plans, education videos, inhaler training videos, feedback to patients and healthcare professionals (HCPs), exacerbation monitoring, and pulmonary rehabilitation. We discuss the key issues with sustaining patient and HCP engagement and limiting attrition of use, interoperability with devices, integration into healthcare systems, and ensuring inclusivity and accessibility. We explore the essential areas of research beyond determining safety and efficacy to understand the acceptability of digital healthcare solutions to patients, clinicians, and healthcare systems, and hence ways to improve this and sustain engagement. Finally, we explore the regulatory challenges to ensure quality and engagement and effective integration into current healthcare systems and care pathways, while maintaining patients’ autonomy and privacy. Understanding and addressing these issues and successful incorporation of an acceptable, simple, scalable, affordable, and future-proof digital solution into healthcare systems could help remodel global chronic disease management and fractured healthcare systems to provide best patient care and optimisation of healthcare resources to meet the global burden and unmet clinical need of COPD.

Blood eosinophil counts in the general population and airways disease: a comprehensive review and meta-analysis

BACKGROUND

The clinical context for using blood eosinophil (EOS) counts as treatment-response biomarkers in asthma and COPD requires better understanding of EOS distributions and ranges. We describe EOS distributions and ranges published in asthma, COPD, control (non-asthma/COPD) and general populations.

METHODS

We conducted a comprehensive literature review and meta-analysis of observational studies (January 2008 to November 2018) that included EOS counts in asthma, severe asthma, COPD, control and general populations. Excluded studies had total sample sizes <200, EOS as inclusion criterion, hospitalised population only and exclusively paediatric participants.

RESULTS

Overall, 91 eligible studies were identified, most had total-population-level data available: asthma (39 studies), severe asthma (12 studies), COPD (23 studies), control (seven studies) and general populations (14 studies); some articles reported data for multiple populations. Reported EOS distributions were right-skewed (seven studies). Reported median EOS counts ranged from 157-280 cells·µL-1 (asthma, 22 studies); 200-400 cells·µL-1 (severe asthma, eight studies); 150-183 cells·µL-1 (COPD, six studies); and 100-160 cells·µL-1 (controls, three studies); and 100-200 cells·µL-1 (general populations, six studies). The meta-analysis showed that observed variability was mostly between studies rather than within studies. Factors reportedly associated with higher blood EOS counts included current smoking, positive skin-prick test, elevated total IgE, comorbid allergic rhinitis, age ≤18 years, male sex, spirometric asthma/COPD diagnosis, metabolic syndrome and adiposity.

CONCLUSION

EOS distribution and range varied by study population, and were affected by clinical factors including age, smoking history and comorbidities, which, regardless of severity, should be considered during treatment decision-making.

Oral corticosteroids stewardship for asthma in adults and adolescents: A position paper from the Thoracic Society of Australia and New Zealand

Oral corticosteroids (OCS) are frequently used for asthma treatment. This medication is highly effective for both acute and chronic diseases, but evidence indicates that indiscriminate OCS use is common, posing a risk of serious side effects and irreversible harm. There is now an urgent need to introduce OCS stewardship approaches, akin to successful initiatives that optimized appropriate antibiotic usage. The aim of this TSANZ (Thoracic Society of Australia and New Zealand) position paper is to review current knowledge pertaining to OCS use in asthma and then delineate principles of OCS stewardship. Recent evidence indicates overuse and over-reliance on OCS for asthma and that doses >1000 mg prednisolone-equivalent cumulatively are likely to have serious side effects and adverse outcomes. Patient perspectives emphasize the detrimental impacts of OCS-related side effects such as weight gain, insomnia, mood disturbances and skin changes. Improvements in asthma control and prevention of exacerbations can be achieved by improved inhaler technique, adherence to therapy, asthma education, smoking cessation, multidisciplinary review, optimized medications and other strategies. Recently, add-on therapies including novel biological agents and macrolide antibiotics have demonstrated reductions in OCS requirements. Harm reduction may also be achieved through identification and mitigation of predictable adverse effects. OCS stewardship should entail greater awareness of appropriate indications for OCS prescription, risk-benefits of OCS medications, side effects, effective add-on therapies and multidisciplinary review. If implemented, OCS stewardship can ensure that clinicians and patients with asthma are aware that OCS should not be used lightly, while providing reassurance that asthma can be controlled in most people without frequent use of OCS.

Asthma and Three Colinear Comorbidities: Obesity, OSA, and GERD

Asthma is a complex disease with heterogeneous phenotypes and endotypes that are incompletely understood. Obesity, obstructive sleep apnea, and gastroesophageal reflux disease co-occur in patients with asthma at higher rates than in those without asthma. Although these diseases share risk factors, there are some data suggesting that these comorbidities have shared inflammatory pathways, drive the development of asthma, or worsen asthma control. This review discusses the epidemiology, pathophysiology, management recommendations, and key knowledge gaps of these common comorbidities.

Predicting asthma-related crisis events using routine electronic healthcare data

BACKGROUND

There is no published algorithm predicting asthma crisis events (Accident and Emergency (A&E) attendance, hospitalisation or death) using routinely available electronic health record (EHR) data.

AIM

To develop an algorithm to identify individuals at high risk of an asthma crisis event.

DESIGN AND SETTING

Database analysis from primary care EHRs.

METHOD

Multivariable logistic regression was applied to a dataset of 61,861 people with asthma from England and Scotland using the Clinical Practice Research Datalink. External validation was performed using the Secure Anonymised Information Linkage databank of 174,240 patients from Wales. Outcomes were one or more hospitalisation (development dataset) and asthma-related hospitalisation, A&E attendance or death (validation dataset) within a 12-month period.

RESULTS

Risk factors for asthma-related crisis events included previous hospitalisation, older age, underweight, smoking and blood eosinophilia. The prediction algorithm had acceptable predictive ability with a Receiver Operating Characteristic (ROC) of 0.71 (0.70, 0.72) in the validation dataset. Using a cut-point based on the 7% of the population at greatest risk results in a positive predictive value of 5.7% (95% CI 5.3 - 6.1) and a negative predictive value of 98.9% (98.9 - 99.0), with sensitivity of 28.5% (26.7 - 30.3) and specificity of 93.3% (93.2 - 93.4); they had an event risk of 6.0% compared 1.1% for the remaining population. Eighteen people would be "needed to follow" to identify one admission.

CONCLUSIONS

This externally validated algorithm has acceptable predictive ability for identifying patients at high risk of asthma-related crisis events and excluding individuals not at high risk.

Developing a short-term prediction model for asthma exacerbations from Swedish primary care patients' data using machine learning - Based on the ARCTIC study

OBJECTIVE

The ability to predict impending asthma exacerbations may allow better utilization of healthcare resources, prevention of hospitalization and improve patient outcomes. We aimed to develop models using machine learning to predict risk of exacerbations.

METHODS

Data from 29,396 asthma patients was collected from electronic medical records and national registers covering clinical and epidemiological factors (e.g. comorbidities, health care contacts), between 2000 and 2013. Machine-learning classifiers were used to create models to predict exacerbations within the next 15 days. Model selection was done using the mean cross validation score of area under precision-recall curve (AUPRC).

RESULTS

The most important predictors of exacerbation were comorbidity burden and previous exacerbations. Model validation on test data yielded an AUPRC = 0.007 (95% CI: ± 0.0002), indicating that historic clinical information alone may not be sufficient to predict a near future risk of asthma exacerbation.

CONCLUSIONS

Supplementation with additional data on environmental triggers, (e.g. weather, pollen count, air quality) and from wearables, might be necessary to improve performance of the short-term predictive model to develop a more clinically useful tool.

Using Computational Methods to Improve Integrated Disease Management for Asthma and Chronic Obstructive Pulmonary Disease: Protocol for a Secondary Analysis

Background

Asthma and chronic obstructive pulmonary disease (COPD) impose a heavy burden on health care. Approximately one-fourth of patients with asthma and patients with COPD are prone to exacerbations, which can be greatly reduced by preventive care via integrated disease management that has a limited service capacity. To do this well, a predictive model for proneness to exacerbation is required, but no such model exists. It would be suboptimal to build such models using the current model building approach for asthma and COPD, which has 2 gaps due to rarely factoring in temporal features showing early health changes and general directions. First, existing models for other asthma and COPD outcomes rarely use more advanced temporal features, such as the slope of the number of days to albuterol refill, and are inaccurate. Second, existing models seldom show the reason a patient is deemed high risk and the potential interventions to reduce the risk, making already occupied clinicians expend more time on chart review and overlook suitable interventions. Regular automatic explanation methods cannot deal with temporal data and address this issue well.

Objective

To enable more patients with asthma and patients with COPD to obtain suitable and timely care to avoid exacerbations, we aim to implement comprehensible computational methods to accurately predict proneness to exacerbation and recommend customized interventions.

Methods

We will use temporal features to accurately predict proneness to exacerbation, automatically find modifiable temporal risk factors for every high-risk patient, and assess the impact of actionable warnings on clinicians’ decisions to use integrated disease management to prevent proneness to exacerbation.

Results

We have obtained most of the clinical and administrative data of patients with asthma from 3 prominent American health care systems. We are retrieving other clinical and administrative data, mostly of patients with COPD, needed for the study. We intend to complete the study in 6 years.

Conclusions

Our results will help make asthma and COPD care more proactive, effective, and efficient, improving outcomes and saving resources.

International Registered Report Identifier (IRRID)

PRR1-10.2196/27065

Long-term prognosis of new adult-onset asthma in obese patients

Background

Obesity has been associated with poor outcomes of asthma in cross-sectional studies, but long-term effect of obesity on asthma remains unknown.

Aims

To study the effects of obesity, found at the time of diagnosis of adult-onset asthma, on 12-year prognosis by focusing on oral corticosteroid (OCS) use and respiratory-related hospital admissions.

Methods

Patients diagnosed with adult-onset asthma (n=203) were divided into three categories based on diagnostic body mass index (BMI) (<25 kg·m⁻², 25–29.9 kg·m⁻², ≥30 kg·m⁻²) and followed for 12 years as part of the Seinäjoki Adult Asthma Study. Self-reported and dispensed OCS were assessed for the 12-year period. Data on hospital admissions were analysed based on medical records.

Results

12 years after diagnosis, 86% of the patients who were obese (BMI ≥30 kg·m⁻²) at diagnosis remained obese. During the follow-up, no difference was found in weight gain between the BMI categories. During the 12-year follow-up, patients obese at diagnosis reported more frequent use of OCS courses (46.9% versus 23.1%, p=0.028), were dispensed OCS more often (81.6% versus 56.9%, p=0.014) and at higher doses (median 1350 (interquartile range 280–3180) mg versus 600 (0–1650) mg prednisolone, p=0.010) compared to normal-weight patients. Furthermore, patients who were obese had more often one or more respiratory-related hospitalisations compared to normal-weight patients (38.8% versus 16.9%, p=0.033). In multivariate logistic regression analyses, obesity predicted OCS use and hospital admissions.

Conclusions

In adult-onset asthma, patients obese at diagnosis mostly remained obese at long-term and had more exacerbations and respiratory-related hospital admissions compared to normal-weight patients during 12-year follow-up. Weight loss should be a priority in their treatment to prevent this outcome.

Obese patients with new adult-onset asthma often remain obese in the long-term and have more exacerbations and respiratory-related hospital admissions during follow-up. High priority should be given to weight loss during treatment to prevent this outcome.https://bit.ly/2G5HtRZ

Characterization of uncontrolled, severe asthma patients with type 2 inflammation (T2): results from a physician survey across countries from Latin American, Eurasian Middle East regions and China

OBJECTIVE

The 2019 GINA guidance incorporates the presence of T2 inflammation in severe asthma patients to determine eligibility for add-on biologic therapy, though little data exists to characterize this population. The objective of this manuscript is to conduct a descriptive analysis to characterize patients with severe asthma in emerging countries based on disease severity, patient exacerbation history, and T2 phenotype.

METHODS

A cross-sectional survey of physicians treating asthma patients ages 12 years and older was conducted in eight countries. Physicians characterized their severe asthma patients and reported data from their patients' medical charts. Medical chart data was selected from the physicians' six most recent asthma patients taking prescription medication.

RESULTS

A total of 550 physicians completed the survey and filled out 3,300 patient record forms. A total of 876 patients have been characterized with uncontrolled severe asthma. Of the 420 patients with available EOS lab data, 40% are indicated with T2 inflammation (EOS ≥150/µL). Ninety-one percent of all patients with available IgE lab data (n = 498) had IgE 30 - 1500 IU/mL indicating allergy-driven asthma. Finally, chronic OCS use (as reported by physicians) was reported in 11% of patients.

CONCLUSION

This research revealed that 65% of patients had at least one of three T2 inflammation comorbidities assessed: allergic rhinitis, chronic rhinosinusitis with nasal polyps, and atopic dermatitis. Discrepancies were observed between patients' treatment regimens and GINA step reported, suggesting there may be room to improve understanding of asthma severity as defined per GINA guidelines as well as asthma control assessment in clinical practice.

Risk Factors Predicting Severe Asthma Exacerbations in Adult Asthmatics: A Real-World Clinical Evidence

PURPOSE

Minimizing the future risk of asthma exacerbation (AE) is one of the main goals of asthma management. We investigated prognostic factors for risk of severe AE (SAE) in a real-world clinical setting.

METHODS

This is an observational study evaluating subjects who were diagnosed with asthma and treated with anti-asthmatic medications from January 1995 to June 2018. Risk factors for SAE were analyzed in 2 treatment periods (during the initial 2 years and the following 3-10 years of treatment) using the big data of electronic medical records.

RESULTS

In this study, 5,058 adult asthmatics were enrolled; 1,335 (28.64%) experienced ≥ 1 SAE during the initial 2 years of treatment. Female sex, higher peripheral eosinophil/basophil counts, and lower levels of forced expiratory volume in 1 second (FEV1; %) were factors predicting the risk of SAEs (P < 0.001 for all). Higher serum total immunoglobulin E levels increased the risk of SAEs among the patients having ≤ 2 SAEs (P = 0.025). Patients with more frequent SAEs during the initial 2 years of treatment had significantly higher risks of SAEs during the following years of treatment (P < 0.001, for all) (patients with ≥ 4 SAEs, odds ratio [OR], 29.147; those with 3 SAEs, OR, 14.819; those with 2 SAEs, OR, 9.867; those with 1 SAE, OR, 5.116), had higher maintenance doses of systemic steroids, and showed more gradual decline in FEV1 (%) and FEV1/forced vital capacity levels maintained during the following years of treatment (P < 0.001 for all).

CONCLUSIONS

Asthmatics having risk factors for SAEs (female sex, higher peripheral eosinophil/basophil counts, and lower FEV1) should be strictly monitored to prevent future risk and improve clinical outcomes.

Towards a personalised treatment approach for asthma attacks

Asthma attacks (exacerbations) are common, accounting for over 90 000 UK hospital admissions per annum. They kill nearly 1500 people per year in the UK, have significant associated direct and indirect costs and lead to accelerated and permanent loss of lung function. The recognition of asthma as a heterogeneous condition with multiple phenotypes has revolutionised the approach to the long-term management of the condition, with greater emphasis on personalised treatment and the introduction of the treatable traits concept. In contrast asthma attacks are poorly defined and understood and our treatment approach consists of bronchodilators and systemic corticosteroids. This review aims to explore the current limitations in the description, assessment and management of asthma attacks. We will outline the risk factors for attacks, strategies to modify this risk and describe the recognised characteristics of attacks as a first step towards the development of an approach for phenotyping and personalising the treatment of these critically important events. By doing this, we hope to gradually improve asthma attack treatment and reduce the adverse effects associated with recurrent courses of corticosteroids.

Real-World Patterns of Pharmacotherapeutic Management of Asthma Patients With Exacerbations in the Spanish National Health System

Background

Little is known about the real1world characteristics of asthma patients with exacerbations or their pharmacotherapeutic management. We described the sociodemographic and clinical characteristics, and the patterns of short and long-term management of asthma attacks, in a population-wide cohort of exacerbators in the region of Valencia, Spain.

Methods

We selected asthma patients with at least one exacerbation in 2015 and 2016, we classified them according to their patterns of exacerbations in the 4 years previous to the index exacerbation and their therapeutic step at baseline based on medication received in the previous year. We described the short and long-term pharmacological management of the index exacerbation.

Results

18,714 patients experienced at least one exacerbation. The majority had no previous exacerbation (46.5%), or exacerbated in only one of the years (26.8%). 2.9% had attacks every single year, 25.7% of whom only received rescue medication at baseline. 29.5% of patients without previous exacerbation received maintenance therapy at baseline. Shortly following the index exacerbation, 2,461 patients (13.1%) did not receive any asthma prescription. Among those treated, 70.3% were prescribed a maintenance therapy, 62.4% received a rescue medication, and 30.5% received an oral corticoid. Throughout the year following the index exacerbation, most patients remained in their baseline therapeutic step.

Conclusions

Most patients that exacerbate present very mild to mild forms of the disease or low levels of treatment and most exacerbations are managed in primary care. These insights may help to refine strategies for improving asthma control in the population.

Predicting the risk of asthma attacks in children, adolescents and adults: protocol for a machine learning algorithm derived from a primary care-based retrospective cohort

INTRODUCTION

Most asthma attacks and subsequent deaths are potentially preventable. We aim to develop a prognostic tool for identifying patients at high risk of asthma attacks in primary care by leveraging advances in machine learning.

METHODS AND ANALYSIS

Current prognostic tools use logistic regression to develop a risk scoring model for asthma attacks. We propose to build on this by systematically applying various well-known machine learning techniques to a large longitudinal deidentified primary care database, the Optimum Patient Care Research Database, and comparatively evaluate their performance with the existing logistic regression model and against each other. Machine learning algorithms vary in their predictive abilities based on the dataset and the approach to analysis employed. We will undertake feature selection, classification (both one-class and two-class classifiers) and performance evaluation. Patients who have had actively treated clinician-diagnosed asthma, aged 8-80 years and with 3 years of continuous data, from 2016 to 2018, will be selected. Risk factors will be obtained from the first year, while the next 2 years will form the outcome period, in which the primary endpoint will be the occurrence of an asthma attack.

ETHICS AND DISSEMINATION

We have obtained approval from OPCRD's Anonymous Data Ethics Protocols and Transparency (ADEPT) Committee. We will seek ethics approval from The University of Edinburgh's Research Ethics Group (UREG). We aim to present our findings at scientific conferences and in peer-reviewed journals.

Using fractional exhaled nitric oxide to guide step-down treatment decisions in patients with asthma: a systematic review and individual patient data meta-analysis

BACKGROUND

High exhaled nitric oxide fraction (F _ENO) levels are associated with greater risk of asthma exacerbation. However, it is not clear how F _ENO can be used to guide safe reductions in inhaled corticosteroid (ICS) doses in asthma patients. This study assesses the ability of F _ENO to guide ICS reductions.

METHODS

Systematic searching of electronic databases identified prospective observational studies and randomised controlled trials which recruited participants with mild-to-moderate asthma aged ≥12 years and measured F _ENO before reducing ICS. We performed multilevel mixed-effects logistic regression in relation to acute exacerbations and estimated each participant's exacerbation risk using our logistic regression model.

RESULTS

We included data from seven out of eight eligible studies, representing 384 participants. ICS doses were halved in four studies and withdrawn in three studies. A baseline F _ENO measurement of ≥50 ppb was associated with increased risk of exacerbations (crude OR 3.14, 95% CI 1.41-7.00, p=0.005; adjusted OR 3.08, 95% CI 1.36-6.98, p=0.007) and corresponded to an estimated exacerbation risk cut-off of 15%. Reducing ICS when estimated exacerbation risk was <15% versus <10% would result in fewer patients remaining on the same ICS dose (40 (10.4%) out of 384 versus 141 (36.7%) out of 384), but similar proportions of patients avoiding exacerbations (222 (91.4%) out of 243, 95% CI 87.1-94.6% versus 311 (90.4%) out of 344, 95% CI 86.8-93.3%).

CONCLUSION

In patients with mild-to-moderate asthma, gradual ICS reduction when F _ENO is <50 ppb may help decrease ICS use without increasing exacerbations. Future research should aim to validate these findings in larger populations.

A primary care database study of asthma among patients with and without opioid use disorders

Substance misuse is associated with poor asthma outcome and death. People with opioid use disorder (OUD) may be at particular risk, however, there have been no case-control studies of asthma care and outcomes in this patient group. A primary care database study of patients with asthma aged 16-65 years was conducted using a matched case-control methodology. The dataset comprised 275,151 adults with asthma, of whom 459 had a clinical code indicating a lifetime history of OUD. Cases with a history of OUD were matched to controls 1:3 by age, gender, smoking status and deprivation index decile. Attendance at annual review (30%) and for immunisation (25%) was poor amongst the overall matched study population (N = 1832). Compared to matched controls, cases were less likely to have attended for asthma review during the previous 12 months (OR = 0.60, 95% CI 0.45-0.80) but had similar immunisation rates. Higher rates of ICS (OR = 1.50, 1.13-1.98) and oral prednisolone use (OR = 1.71, 1.25-2.40) were seen amongst those with a history of OUD and 7.2% had a concurrent diagnosis of COPD (OR = 1.86, 1.12-2.40). We found that people with asthma and a history of OUD have worse outcomes on several commonly measured metrics of asthma care. Further research is required to identify reasons for these findings, the most effective strategies to help this vulnerable group access basic asthma care, and to better understand long-term respiratory outcomes.

Treatment patterns among non-active users of maintenance asthma medication in the United Kingdom: a retrospective cohort study in the Clinical Practice Research Datalink

Objective: To describe patient characteristics, treatment patterns and healthcare utilization (HCU) of non-active users of maintenance asthma medications in the United Kingdom.Methods: Retrospective, cohort analysis of patients with asthma, aged ≥ 6 years who were non-active users of maintenance therapy (no prescription for inhaled corticosteroids (ICS), combined ICS/long-acting beta agonists (ICS/LABA) or 'other' bronchodilatory therapies in last 12 months) were identified in the Clinical Practice Research Datalink (2012-2015) and followed-up for 2 years after a new prescription for an asthma maintenance medication (index date). Patient characteristics, most common maintenance treatment sequences and HCU were described.Results: 55,293 patients were identified (ICS: 46,297, ICS/LABA: 8,367; Other: 629). Mean age was 37 years and 56% were female. During follow-up, the most common treatment sequences across groups implied intermittent use, comprising periods of maintenance therapy interspersed with maintenance-free periods. During year 1 and year 2 of follow-up, the proportion of patients prescribed OCS was 19% and 13%, prescribed ≥ 4 short-acting bronchodilators (SABD) was 24% and 19%, having ≥ 3 asthma-related primary care consultations/year was 59% and 36% and experiencing ≥ 1 exacerbation/year was 15% and 11%, respectively.Conclusions: In previously non-active users of asthma maintenance medication subsequently commenced on maintenance therapy, intermittent use was common during the 2-year follow-up despite the potential need for regular use as evidenced by patient HCU and SABD usage patterns. This highlights the need for regular patient assessment and education on medication adherence to ensure appropriateness of prescribing to maintain asthma control.

A systematic review of methodology used in the development of prediction models for future asthma exacerbation

Background

Clinical prediction models are widely used to guide medical advice and therapeutic interventions. Asthma is one of the most common chronic diseases globally and is characterised by acute deteriorations. These exacerbations are largely preventable, so there is interest in using clinical prediction models in this area. The objective of this review was to identify studies which have developed such models, determine whether consistent and appropriate methodology was used and whether statistically reliable prognostic models exist.

Methods

We searched online databases MEDLINE (1948 onwards), CINAHL Plus (1937 onwards), The Cochrane Library, Web of Science (1898 onwards) and ClinicalTrials.gov, using index terms relating to asthma and prognosis. Data was extracted and assessment of quality was based on GRADE and an early version of PROBAST (Prediction study Risk of Bias Assessment Tool). A meta-analysis of the discrimination and calibration measures was carried out to determine overall performance across models.

Results

Ten unique prognostic models were identified. GRADE identified moderate risk of bias in two of the studies, but more detailed quality assessment via PROBAST highlighted that most models were developed using highly selected and small datasets, incompletely recorded predictors and outcomes, and incomplete methodology. None of the identified models modelled recurrent exacerbations, instead favouring either presence/absence of an event, or time to first or specified event. Preferred methodologies were logistic regression and Cox proportional hazards regression.

The overall pooled c-statistic was 0.77 (95% confidence interval 0.73 to 0.80), though individually some models performed no better than chance. The meta-analysis had an I² value of 99.75% indicating a high amount of heterogeneity between studies. The majority of studies were small and did not include internal or external validation, therefore the individual performance measures are likely to be optimistic.

Conclusions

Current prognostic models for asthma exacerbations are heterogeneous in methodology, but reported c-statistics suggest a clinically useful model could be created. Studies were consistent in lacking robust validation and in not modelling serial events. Further research is required with respect to incorporating recurrent events, and to externally validate tools in large representative populations to demonstrate the generalizability of published results.

Developing a Model to Predict Hospital Encounters for Asthma in Asthmatic Patients: Secondary Analysis

Background

As a major chronic disease, asthma causes many emergency department (ED) visits and hospitalizations each year. Predictive modeling is a key technology to prospectively identify high-risk asthmatic patients and enroll them in care management for preventive care to reduce future hospital encounters, including inpatient stays and ED visits. However, existing models for predicting hospital encounters in asthmatic patients are inaccurate. Usually, they miss over half of the patients who will incur future hospital encounters and incorrectly classify many others who will not. This makes it difficult to match the limited resources of care management to the patients who will incur future hospital encounters, increasing health care costs and degrading patient outcomes.

Objective

The goal of this study was to develop a more accurate model for predicting hospital encounters in asthmatic patients.

Methods

Secondary analysis of 334,564 data instances from Intermountain Healthcare from 2005 to 2018 was conducted to build a machine learning classification model to predict the hospital encounters for asthma in the following year in asthmatic patients. The patient cohort included all asthmatic patients who resided in Utah or Idaho and visited Intermountain Healthcare facilities during 2005 to 2018. A total of 235 candidate features were considered for model building.

Results

The model achieved an area under the receiver operating characteristic curve of 0.859 (95% CI 0.846-0.871). When the cutoff threshold for conducting binary classification was set at the top 10.00% (1926/19,256) of asthmatic patients with the highest predicted risk, the model reached an accuracy of 90.31% (17,391/19,256; 95% CI 89.86-90.70), a sensitivity of 53.7% (436/812; 95% CI 50.12-57.18), and a specificity of 91.93% (16,955/18,444; 95% CI 91.54-92.31). To steer future research on this topic, we pinpointed several potential improvements to our model.

Conclusions

Our model improves the state of the art for predicting hospital encounters for asthma in asthmatic patients. After further refinement, the model could be integrated into a decision support tool to guide asthma care management allocation.

International Registered Report Identifier (IRRID)

RR2-10.2196/resprot.5039

Exacerbation Patterns in Adults with Asthma in England. A Population-based Study

RATIONALE

Asthma is heterogeneous and knowledge on exacerbation patterns is lacking. Previous studies have had a relatively short follow-up or focused on severe disease.

OBJECTIVES

To describe exacerbation patterns over a prolonged follow-up in a population that includes patients of all disease severity.

METHODS

We used electronic health care records to identify patients with asthma aged 18-55 years and their exacerbations from 2007 to 2015. A cohort with greater than or equal to 7 years of data was used to describe exacerbation patterns by asthma severity defined by medication use. Effect estimates for risk factors were calculated for sporadic (single year of exacerbations) and recurrent (>1 yr) exacerbation patterns, using logistic regression. In a nested case-control design, the association between a history of exacerbations, spanning 5 years, and a future exacerbation was examined.

MEASUREMENTS AND MAIN RESULTS

A total of 51,462 patients were eligible for the 7-year cohort; 64% had no exacerbations. Of those who exacerbated, 51% did so only once; exacerbation frequency increased with disease severity. Only 370 patients (0.7%) were characterized by a frequent-exacerbator phenotype (yearly exacerbations), of whom 58% had mild/moderate asthma. Exacerbation risk factors were not uniquely associated with a particular exacerbation pattern. A past exacerbation increased the risk of a future exacerbation more than all other factors, although this effect dissipated over 5 years.

CONCLUSIONS

During 7 years of follow-up, exacerbations occur in around one-third of patients. Of those who exacerbate, half do not do so again; the timing of future exacerbations is largely unpredictable. Just 2% exhibit a frequent-exacerbator phenotype. Past exacerbation patterns are the most informative risk factor for predicting future exacerbations.